Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

• Main Authors: Tianjiang Hu, Shuyuan Wang, Han Zhou, Guangming Wang, Daibing Zhang
• Format: Article
• Language: English
• Published: SAGE Publishing 2016-06-01
• Series: International Journal of Advanced Robotic Systems
• Online Access: https://doi.org/10.5772/63632

This article offers new insights on the learning control approach developed by [Hu et al. IEEE/ASME Trans. Mechatronics, 19(1): 191–200, 2014]. Theoretical insights are further proposed to unveil why the contraction-type iterative learning control (ILC) schemes are suitable and effective in compensating for hysteresis, widely existing in biorobotic locomotion. Under such circumstances, iteration-based second-order dynamics is adopted to describe the biorobotic systems acted upon by one unknown Preisach hysteresis term. The memory clearing operator is mathematically proven to enable feasibility of contraction-type ILC methods, regardless of whether the initial state is accurately set or not. The simulation examples confirm that the developed iteration-based controller combined with a preceded operator effectively reduce tracking errors caused by the hysteresis nonlinearity. Furthermore, the new insights on theoretical feasibility are definitively corroborated in accordance with the previously published experimental results.